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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Lithiation-Induced Defect Engineering to Promote Oxygen Evolution Reaction

Exploring efficient electrocatalysts for oxygen evolution reaction (OER) is an urgent need to advance the development of sustainable energy conversion. Though defect engineering is considered an effective strategy to regulate catalyst activity for enhanced OER performance, the controllable synthesis of defective oxides electrocatalysts remains challenging. Here, oxygen defects are introduced into NiCo 2 O 4 nanorods by an electrochemical lithiation strategy. By tuning in situ lithiation potentials, the concentration of oxygen defects and the corresponding catalytic activity can be feasibly regulated. In addition, the relationship between the changes in the defect density and electronic structure and the lithiation cut-off voltages is revealed. The results show that NiCo 2 O 4 nanorods undertook intercalation and two-step conversion reaction, in which the lithiation-induced conversion reaction gives rise to a CoO@NiO-based structure with higher defect density and lower oxidation states. As a result, the defective CoO@NiO-based catalyst exhibits exceptional OER activity with an overpotential of 270 mV at 10 mA cm -2 , which is about 74 mV below the pristine nanomaterials. In conclusion, this research proposes a novel strategy to explore high-performance catalysts with structural stability and defect control.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Physical and Functional Constraints on Viable Belowground Acquisition Strategies

Since their emergence onto land, terrestrial plants have developed diverse strategies to acquire soil resources. However, we lack a framework that adequately captures how these strategies vary among species. Observations from around the world now allow us to quantify the variation observed in commonly-measured fine-root traits but it is unclear how root traits are interrelated and whether they fall along an “economic” spectrum of acquisitive to conservative strategies. We assessed root trait variation and mycorrhizal colonization rates by leveraging the largest global database of fine-root traits (the Fine-Root Ecology Database; FRED). We also developed a heuristic model to explore the role of mycorrhizal fungi in defining belowground exploration efficiency across a gradient of thin- to thick-diameter roots. In support of the expectations of the “root economic spectrum,” we found that root diameter was negatively related to specific root length (Pearson’s r=-0.76). However, we found an unexpected negative relationship between root diameter and root tissue density (Pearson’s r = -0.40), and we further observed that root nitrogen content was largely unrelated to other economic traits. Mycorrhizal colonization was most closely associated with root diameter (Pearson’s r = 0.62) and was unrelated to root tissue density and root nitrogen. The heuristic model demonstrated that while thinner roots have inherently greater capacity to encounter soil resources based on higher surface area per unit mass, the potential for increased associations with mycorrhizal fungi in thicker roots, combined with greater hyphal growth, can result in equally acquisitive strategies for both thin- and thick roots. Taken together, our assessments of root trait variation, trade-offs with mycorrhizal fungi, and broader connections to root longevity allowed us to propose a series of fundamental constraints on belowground resource acquisition strategies. Physical tradeoffs based on root construction (i.e., economic traits) and functional limitations related to the capacity of a root to encounter and acquire soil resources combine to limit the two-dimensional belowground trait space. Within this trait space there remains a diversity of additional variation in root traits that facilitates a wide range of belowground resource acquisition strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Broadband Asymmetric Transmission of Linearly Polarized Mid‐Infrared Light Based on Quasi‐3D Metamaterials

Abstract Metamaterials consisting of subwavelength resonators offer an exciting opportunity for realizing asymmetric transmission (AT) of linearly polarized light. However, to date, only moderate/narrow‐band AT responses have been obtained in metadevices based on stacked planar nanostructures. Here, leveraging a combination of a genetic algorithm (GA) based optimization method and a membrane projection lithography (MPL) fabrication approach, a quasi‐3D metamaterial for broadband AT of linearly polarized mid‐infrared light is demonstrated. Facilitated by the customized GA, an efficient exploration of 3D plasmonic meta‐atoms with broken mirror symmetry in the light propagation direction allows the satisfaction of the rigorous conditions for AT of linearly polarized waves over a broad wavelength range. Confirmed by surface current analysis, the observed AT behavior is attributed to the resonant coupling between the plasmonic nanostructures located on the two orthogonal walls of the MPL cavities. Incorporating an advanced inverse‐design method and a state‐of‐art fabrication technique, the methodology used in the present study provides a promising route for exploiting 3D metamaterials with sophisticated functionalities via effectively exploring the high‐dimensional parametric space offered by true 3D meta‐atoms.

Whiting, Eric B.↗
DOE OSTI2024

Performance Prediction of High‐Entropy Perovskites La 0.8 Sr 0.2 Mn x Co y Fe z O 3 with Automated High‐Throughput Characterization of Combinatorial Libraries and Machine Learning

Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition–structure–performance relationships of high-entropy La 0.8 Sr 0.2 Mn x Co y Fe z O 3±𝞭 perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. Following the deposition of a continuous compositional map using thin-film combinatorial pulsed laser deposition, compositional, structural, and performance properties are characterized using six different techniques with mapping capabilities. Random forests effectively model electrochemical performance, consistently identifying Fe-rich oxides as optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700 °C. Additionally, the models identify a statistical correlation between oxygen sublattice distortion—derived from spectral analysis of Raman-active modes—and enhanced performance.

high entropy oxides↗
DOE OSTI2025

A Route to Design Novel Functional Peptides by Applying a Denoising Diffusional Model to mRNA Display Libraries

In vitro directed evolution techniques, such as mRNA display, enable peptide ligand discovery and optimization. However, physical libraries that rely on a genetic code can only search a small fraction of sequence space due to inherent biases in the genetic code and experimental limitations. To address this challenge, denoising diffusion implicit models (DDIMs) are applied to generate novel peptide ligands against B‐cell lymphoma extra‐large (Bcl‐x L ), a key cancer target. Starting with high‐throughput sequencing data from previous selections, a DDIM is trained to produce novel sequences with high affinity binding. Experimental validation confirms that most generated sequences are functionally equivalent to the original library members for Bcl‐x L binding and demonstrated comparable binding kinetics and affinity relative to the wildtype and nearest original neighbors. Importantly, this approach generated rare sequences not easily accessible via mutation and directed evolution. These results indicate that DDIMs can complement and expand directed evolution data, efficiently exploring underrepresented regions of sequence space. This approach provides a broadly applicable framework for accelerating ligand discovery and optimizing molecular properties across diverse targets.

Qi, Pearl [Mork Family Department of Chemical Engi↗
DOE OSTI2021

Nickel-based superalloy single crystals fabricated via electron beam melting

Additive manufacturing technologies have emerged as potential disruptive processes whose possible impacts range from supply chain logistics, prototyping, and novel materials synthesis. Numerous works illustrate the ability to control mi- crostructure in fusion based processes and a few recent authors have even produced single crystals. However, a number of open questions remain regarding the process window which enables printing of single crystals. Furthermore, it has been observed that these additively manufactured single crystals exhibit a preferred $\langle$011$\rangle$ secondary orientation. In this work we investigate fabrication conditions which enable printing of single crystals via electron beam melting. A space filling design of experiments is utilized to efficiently explore the fabrication space. Single crystals were success- fully obtained using both commercially available powders and custom melt alloys. Microstructures obtained via these exploratory experiments exhibited a continuum of columnar structures ranging from weakly textured polycrystals, near single crystal, and fully single crystalline material. Complex geometry experiments are performed to study the grain selection mechanism. We find that the grain selection mechanism is independent of the bulk scale geometry and must therefore by driven by local heat transfer and solidification dynamics. Furthermore, grain selection is shown to be driven by competing driving forces; one which prefers epitaxial growth and another which is driven by the imposed scan pattern geometry. Finally, a mechanism is proposed for the anomalous secondary orientation preference.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys

We report Bayesian Optimization (BO) has emerged as a powerful framework to efficiently explore and exploit materials design spaces. To date, most BO approaches to materials design have focused on the materials discovery problem as if it were a single expensive-to-query ‘black box’ in which the target is to optimize a single objective (i.e., material property or performance metric). Also, such approaches tend to be constraint agnostic. Here, we present a novel multi-information BO framework capable of actively learning materials design as a multiple objectives and constraints problem. We demonstrate this framework by optimally exploring a Refractory Multi-Principal-Element Alloy (MPEA) space, here specifically, the system Mo-Nb-Ti-V-W. The MPEAs are explored to optimize two density-functional theory (DFT) derived ductility indicators (Pugh’s Ratio and Cauchy pressure) while learning design constraints relevant to the manufacturing of high-temperature gas-turbine components. Alloys in the BO Pareto-front are analyzed using DFT to gain an insight into fundamental atomic and electronic underpinning for their superior performance, as evaluated within this framework.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interface morphology and dislocation-mediated processes during rapid solidification of thin films

Rapid solidification experiments have, in recent years, revealed a wealth of new microstructural phenomena that suggest a strong connection between the kinetics of solidification and the crystalline structures that emerge as a result. In this work, we investigate the interplay between interface morphology and defect-mediated processes during rapid solidification conditions using a Phase Field Crystal (PFC) model, enabling us to simultaneously and efficiently explore the physics of solidification and elasto-plasticity in the formalism of a single-field theory. We predict that there are two mechanisms by which dislocations emitted directly from the solid–liquid interface induce orientation gradients as well as the formation of subgrain boundaries within a single solidifying cell. We relate these mechanisms to the morphology of the moving solid–liquid interface and identify a suitable control parameter in the PFC model with which we can go between said morphologies by effectively changing the relative strength of the capillary length and kinetic coefficients of the solid–liquid interface. Thus, we are able to provide mechanistic explanations for several microstructural features (with an emphasis on orientation gradients and subgrain boundaries) observed during the rapid solidification of pure materials. We also provide a simple explanation for the formation of “jagged” subgrain boundaries, which is consistent with our experimental observations in rapidly solidified samples of Aluminum, whose mechanisms have thus far been unknown.

Interface morphology↗
DOE OSTI2026

Machine learning insights into microstructural origins of transport and mechanical properties in porous microstructures

Multifunctional porous materials are increasingly needed across various fields, but their complex microstructures create significant challenges due to the intricate microstructure-property relationships. This complexity, combined with limitations of traditional analysis methods, hinders efforts to understand and optimize microstructure–property relationships. Here, to address this, we integrate physics-based mesoscale modeling with interpretable machine learning (ML) to uncover how microstructural features govern effective diffusivity and elastic modulus. At constant porosity, we show diffusivity varies by over 150 × and modulus by ∼50 ×, highlighting the power of microstructure engineering. Statistical analysis reveals bimodal behavior in diffusivity and unimodal in modulus. ML identifies connectivity as the dominant factor, while modulus is also sensitive to domain size and feature interactions. Controlled simulations further highlight domain shape as a critical feature for modulus. This framework enables efficient exploration of microstructure-property correlations, offering new insights to guide the design of advanced porous materials.

Bicontinuous microstructure↗
DOE OSTI2025

Genetic algorithm optimization of nuclear criticality experiment for reduction of intermediate-energy 239 Pu nuclear data uncertainties

Nuclear criticality experiments are conducted to investigate specific nuclear data important for safe handling and storage of fissile materials, reactor design and operation, and the validation of radiation transport codes. Incorrect or uncertain nuclear data can prohibitively impact operational safety limits, reactor licensing, and predictive simulation capability; therefore, integral measurements from criticality experiments are necessary and should be performed frequently. To maximize the impact of the integral measurements, it is important to consider experiment geometry, material selection, and component dimensions. When taking these considerations into account, the experiment design process becomes iterative and very time intensive. This work utilizes a genetic algorithm to efficiently explore potential nuclear criticality experiment designs for the Laboratory Directed Research & Development project PARADIGM (PARallel Approach of Differential and InteGral Measurements) at Los Alamos National Laboratory. In this paper, the building blocks of the genetic algorithm are discussed in detail, the genetic algorithm methodology is verified, and the genetic algorithm is used to produce three candidate experiment models for the final PARADIGM design. The three candidate models produced by the genetic algorithm consist of copper-reflected assemblies containing 14 repeating units of alumina, graphite, boron, and plutonium plates. Furthermore, in addition to the optimization results, final design considerations are also discussed for designs with a height and/or weight very close to or slightly above assembly machine operational limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A new self-adaptive reconstruction method to identify defects through Wigner–Seitz approach

A new self-adaptive reconstruction method based on local atomic structure at any given molecular dynamics (MD) step has been developed in this article. The method can be used in Wigner–Seitz defect analysis approach to correctly and efficiently explore the information of both point defects and complex defect clusters (e.g. dislocation loops and voids) formed after a displacement cascade where the cascade interacts with grain boundaries and/or dislocations. The algorithm and validation are provided in detail. Results for identification of radiation defects during and after cascades interacting with a dislocation network show that the new method can well recognize all simple and complex defects and defect clusters. Thus, this new method provides a totally new way to explore the density and size of radiation defects at atomic scale after complex MD evolution processes, providing correct information to understand and predict radiation damage in materials through atomic simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Biogas Reforming to Syngas: A Review

Interest in novel uses of biogas has increased recently due to concerns about climate change and greater emphasis on renewable energy sources. Although biogas is frequently used in low-value applications such as heating and fuel in engines or even just flared, reforming is an emerging strategy for converting biogas to syngas, which could then be used to obtain high-value-added liquid fuels and chemicals. Interest also exists due to the role of dry, bi-, and tri-reforming in the capture and utilization of CO 2 . New research efforts have explored efficient and effective reforming catalysts, as specifically applied to biogas. In this paper, we review recent developments in dry, bi-, and tri-reforming, where the CO 2 in biogas is used as an oxidant/partial oxidant. The synthesis, characterization, lifetime, deactivation, and regeneration of candidate reforming catalysts are discussed in detail. The thermodynamic limitation and techno-economics of biogas conversion are also discussed.

09 BIOMASS FUELS↗
DOE OSTI2021

Convergence of micro-geochemistry and micro-geomechanics towards understanding proppant shale rock interaction: A Caney shale case study in southern Oklahoma, USA

As a direct outcome of economic development coupled with an increase in population, global energy demand will continue to rise in the coming decades. Although renewable energy sources are increasingly investigated for optimal production, the immediate needs require focus on energy sources that are currently available and reliable, with a minimal environmental impact; the efficient exploration and production of unconventional hydrocarbon resources is bridging the energy needs and energy aspirations, during the current energy transition period. The main challenges are related to the accurate quantification of the critical rock properties that influence production, their heterogeneity and the multiscale driven physico-chemical nature of rock–fluid interactions. A key feature of shale reservoirs is their low permeability due to dominating nanoporosity of the clay-rich matrix. As a means of producing these reservoirs in a cost-effective manner, a prerequisite is creation of hydraulic fracture networks capable of the highest level of continued conductivity. Fracturing fluid chemical design, formation brine geochemical composition, and rock mineralogy all contribute to swelling-induced conductivity damage. The Caney Shale is an organic-rich, often calcareous mudrock. Many studies have examined the impact that clay has on different kinds of shale productivity but there is currently no data reported on the Caney Shale in relation to horizontal drilling; all reported data on the Caney Shale is on vertical wells which are shallow, compared to an emerging play that is at double the depth. Here, in this work we develop geochemical–geomechanical integration of rock properties at micro-and nanoscales that can provide insights into the potential proppant embedment and its mitigation. The novel methodology amalgamates the following: computed X-ray tomography, scanning electron microscopy, energy dispersive spectroscopy, micro-indentation, and Raman spectroscopy techniques. Here, our results show that due to the multiscale heterogeneity in the Caney Shale, these geochemical and structural properties translate into a variation in mechanical properties that will impact interaction between the proppant and the host shale rock.

03 NATURAL GAS↗
DOE OSTI2020

On the use of charged particles to characterize precipitation in irradiated reactor pressure vessel steels with a wide range of compositions

Nuclear reactor lifetimes may be limited by nano-scale Cu-Mn-Ni-Si precipitates (CRPs and MNSPs) that form under neutron irradiation (NI) of pressure vessel (RPV) steels, resulting in hardening and ductile to brittle transition temperature increases (embrittlement). Physical models of embrittlement must be based on characterization of precipitation as a function of the combination of metallurgical and irradiation variables. Here we focus on rapid and convenient charged particle irradiations (CPI) to both: a) compare to precipitates formed in NI; and, b) use CPI to efficiently explore precipitation in steels with a very wide range of compositions. Atom probe tomography (APT) comparisons show NI and CPI for similar bulk steel solute contents yield nearly the same precipitate compositions, albeit with some differences in their number density, size and volume fraction (f) dose (dpa) dependence. Yet, the overall precipitate evolutions are very similar. Advanced high Ni (>3 wt%) RPV steels, with superior unirradiated properties, were also investigated at high CPI dpa. For typical Mn contents, MNSPs have Ni 16 Mn 6 Si 7 or Ni 3 Mn 2 Si phase type compositions, with f values that are close to the equilibrium phase separated values. In contrast, in steels with very low Mn and high Ni, Ni 2-3 Si silicide phase type precipitate compositions are observed; and when Ni is low, the precipitate compositions are close to the MnSi phase field. Low Mn significantly reduces, but does not eliminate, precipitation in high Ni steels. A comparison of dispersed barrier model predictions with measured hardening data suggests that the Ni-Si dominated precipitates are weaker dislocation obstacles than the G phase type MNSPs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

The confluence of machine learning and multiscale simulations

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. In conclusion, this confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides

Precise determination of atomic structural information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material characterization; however, extracting chemical information from experimental probes remains a significant challenge, particularly for disordered materials. We present an integrated approach that combines atomic simulations, data-driven techniques, and experimental measurements to investigate chemical speciation of amorphous carbon nitride systems as a case study. Here, we discuss the development of machine learning potentials that can efficiently explore the vast configuration space of amorphous carbon nitrides. By employing statistical methods, this structural database enables the elucidation of the most representative local structures and how they evolve with chemical compositions and density. Density functional theory simulations are used to establish a correlation between the local structure and spectroscopic signatures, which then serve as the basis for interpreting and extracting chemical content from experimental data. Although our framework is specifically demonstrated for XANES and carbon nitrides, the approach described herein is readily adaptable as applied to other experimental characterization probes and materials classes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine Learning-Guided Discovery of Ternary Compounds Containing La, P, and Group 14 Elements

In this work, we integrate a deep machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary compounds. Using La–Si–P as a prototype system, we demonstrate that ML-guided first-principles calculations can efficiently explore crystal structures and their relative energetic stabilities, thus greatly accelerate the pace of material discovery. A number of new La–Si–P ternary compounds with formation energies less than 30 meV/atom above the known ternary convex hull are discovered. Among them, the formation energies of La 5 SiP 3 and La 2 SiP phases are only 2 and 10 meV/atom, respectively, above the convex hull. These two compounds are dynamically stable with no imaginary phonon modes. Moreover, by replacing Si with heavier-group 14 elements in the eight lowest-energy La–Si–P structures from our ML-guided predictions, a number of low-energy La–X–P phases (X = Ge, Sn, Pb) are predicted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗